Abstract
In this paper, a novel envelope-constraint-based neural controller is addressed for air-breathing hypersonic vehicles with unknown non-affine formulations. The vehicle dynamics is decomposed into velocity subsystem and altitude subsystem that are further described as unknown non-affine expressions. Neural networks are employed to approximate the unknown terms of each subsystem based on Mean Value Theorem, which rejects system uncertainties and non-affine characteristics. New learning algorithms are developed for adaptive parameters to stabilize the closed-loop system and reduce online parameters, yielding a low computational load design. The remarkable advantages are that complicated design procedure of back-stepping is avoided, and desired transient performance is guaranteed for velocity and altitude tracking errors via envelope constraint. The stability of closed-loop control system is proved utilizing Barrier Lyapunov functions. Finally, numerical simulation results are presented to validate the effectiveness of the proposed control approach.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
